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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Related Experiment Video

Updated: Jan 9, 2026

Benefits of Cardiac Resynchronization Therapy in an Asynchronous Heart Failure Model Induced by Left Bundle Branch Ablation and Rapid Pacing
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Deep Learning-Based Prediction Model for Cardiac Resynchronization Therapy Responders Using Electrocardiogram Data.

Hitoshi Mori1, Yuya Fujisaki2, Syunta Higuchi3

  • 1Department of Cardiology, Saitama Medical University International Medical Center, Hidaka, Japan.

Journal of Cardiovascular Electrophysiology
|December 3, 2025
PubMed
Summary

Deep learning models using electrocardiogram (ECG) data can predict cardiac resynchronization therapy (CRT) response. This artificial intelligence approach may improve patient selection for CRT, enhancing heart failure management.

Keywords:
cardiac resynchronization therapydeep learningelectrocardiogramresponder

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cardiac resynchronization therapy (CRT) is a key treatment for advanced heart failure.
  • A significant portion of patients (approx. 30%) do not respond to CRT.
  • Predicting CRT response is crucial for optimizing patient outcomes.

Purpose of the Study:

  • To develop and evaluate deep learning models for predicting CRT response.
  • To utilize preimplantation electrocardiogram (ECG) data for predictive modeling.
  • To assess the efficacy of different AI model architectures.

Main Methods:

  • Retrospective analysis of 285 patients undergoing CRT implantation.
  • Definition of CRT responders based on ≥15% left ventricular end-systolic volume reduction.
  • Development of three models: ResNet-18 (ECG images), SSL-enhanced ResNet-18, and LightGBM (time-series ECG data).

Main Results:

  • The SSL-enhanced ResNet-18 model showed stable performance (78.5% accuracy, 81.3% PPV).
  • The LightGBM model achieved the highest accuracy (79.4%), while ResNet-18 had the highest PPV (84.2%).
  • Model interpretability (Grad-CAM) revealed varied focus on ECG leads, suggesting diverse predictive features.

Conclusions:

  • AI models utilizing preimplantation ECG data can effectively predict CRT responders.
  • Image-based deep learning models show promise in CRT response prediction.
  • This AI-driven approach can enhance patient selection and personalize CRT strategies.